@article{Anderson2003,
author = {Anderson, R P and Lew, D and Peterson, A Townsend},
journal = {Ecol. Model.},
pages = {211--232},
title = {{Evaluating predictive models of species' distributions: Criteria for selecting optimal models}},
volume = {162},
year = {2003}
}


@article{Barve2011a,
author = {Barve, Narayani and Barve, Vijay and Jim{\'{e}}nez-Valverde, Alberto and Lira-Noriega, Andr{\'{e}}s and Maher, Sean P. and Peterson, a. Townsend and Sober{\'{o}}n, Jorge and Villalobos, Fabricio},
doi = {10.1016/j.ecolmodel.2011.02.011},
issn = {03043800},
journal = {Ecol. Modell.},
month = {jun},
number = {11},
pages = {1810--1819},
publisher = {Elsevier B.V.},
title = {{The crucial role of the accessible area in ecological niche modeling and species distribution modeling}},
volume = {222},
year = {2011}
}


@article{Booth2014,
author = {Booth, Trevor H. and Nix, Henry A. and Busby, John R. and Hutchinson, Michael F.},
doi = {10.1111/ddi.12144},
editor = {Franklin, Janet},
file = {::},
issn = {13669516},
journal = {Divers. Distrib.},
keywords = {Biogeography,bioclimate envelope,biological conservation,climate change,climate interpolation,ecological modelling,ecological niche},
month = {jan},
number = {1},
pages = {1--9},
publisher = {Wiley/Blackwell (10.1111)},
title = {{bioclim: the first species distribution modelling package, its early applications and relevance to most current MaxEnt studies}},
url = {http://doi.wiley.com/10.1111/ddi.12144},
volume = {20},
year = {2014}
}


@incollection{Busby1991,
address = {Canberra, Australia},
annote = {NOT IN FILE
10},
author = {Busby, J R and Margules, C R and Austin, M P},
booktitle = {Nat. Conserv. Cost-effective Biol. Surv. Data Anal.},
editor = {Margules, C R and Austin, M P},
keywords = {Bioclim,Conservation,analysis,cost,data,nature,prediction system},
pages = {64},
pmid = {782},
title = {{BIOCLIM - A bioclimate analysis and prediction system}},
year = {1991}
}


@article{Cobos2019,
 title = {kuenm: an R package for detailed development of ecological niche models using Maxent},
 author = {Cobos, Marlon E. and Peterson, A. Townsend and Barve, Narayani and Osorio-Olvera, Luis},
 year = 2019,
 month = feb,
 keywords = {Extrapolation risks, Model calibration, Model projections, Model selection, Species distribution models},
 abstract = {

               Background
               Ecological niche modeling is a set of analytical tools with applications in diverse disciplines, yet creating these models rigorously is now a challenging task. The calibration phase of these models is critical, but despite recent attempts at providing tools for performing this step, adequate detail is still missing. Here, we present the kuenm R package, a new set of tools for performing detailed development of ecological niche models using the platform Maxent in a reproducible way.


               Results
               This package takes advantage of the versatility of R and Maxent to enable detailed model calibration and selection, final model creation and evaluation, and extrapolation risk analysis. Best parameters for modeling are selected considering (1) statistical significance, (2) predictive power, and (3) model complexity. For final models, we enable multiple parameter sets and model transfers, making processing simpler. Users can also evaluate extrapolation risk in model transfers via mobility-oriented parity (MOP) metric.


               Discussion
               Use of this package allows robust processes of model calibration, facilitating creation of final models based on model significance, performance, and simplicity. Model transfers to multiple scenarios, also facilitated in this package, significantly reduce time invested in performing these tasks. Finally, efficient assessments of strict-extrapolation risks in model transfers via the MOP and MESS metrics help to prevent overinterpretation in model outcomes.

         },
 volume = 7,
 pages = {e6281},
 journal = {PeerJ},
 issn = {2167-8359},
 url = {https://doi.org/10.7717/peerj.6281},
 doi = {10.7717/peerj.6281}
}



@article{Colwell2009,
abstract = {10.1073/pnas.0901650106 The duality between “niche” and “biotope” proposed by G. Evelyn Hutchinson provides a powerful way to conceptualize and analyze biogeographical distributions in relation to spatial environmental patterns. Both Joseph Grinnell and Charles Elton had attributed niches to environments. Attributing niches, instead, to species, allowed Hutchinson's key innovation: the formal severing of physical place from environment that is expressed by the duality. In biogeography, the physical world (a spatial extension of what Hutchinson called the biotope) is conceived as a map, each point (or cell) of which is characterized by its geographical coordinates and the local values of environmental attributes at a given time. Exactly the same environmental attributes define the corresponding niche space, as niche axes, allowing reciprocal projections between the geographic distribution of a species, actual or potential, past or future, and its niche. In biogeographical terms, the realized niche has come to express not only the effects of species interactions (as Hutchinson intended), but also constraints of dispersal limitation and the lack of contemporary environments corresponding to parts of the fundamental niche. Hutchinson's duality has been used to classify and map environments; model potential species distributions under past, present, and future climates; study the distributions of invasive species; discover new species; and simulate increasingly more realistic worlds, leading to spatially explicit, stochastic models that encompass speciation, extinction, range expansion, and evolutionary adaptation to changing environments.},
author = {Colwell, Robert K. and Rangel, Thiago F.},
doi = {10.1073/pnas.0901650106},
isbn = {1091-6490},
issn = {0027-8424},
journal = {Proc. Natl. Acad. Sci. USA},
number = {2},
pages = {19651--19658},
pmid = {19805163},
title = {{Hutchinson's duality: The once and future niche}},
volume = {106},
year = {2009}
}


@article{Elith2010,
author = {Elith, Jane and Kearney, Michael and Phillips, Steven},
journal = {Methods Ecol. Evol.},
number = {4},
pages = {330--342},
title = {{The art of modelling range-shifting species}},
volume = {1},
year = {2010}
}


@article{FIELDING1997,
abstract = {Predicting the distribution of endangered species from habitat data is frequently perceived to be a useful technique. Models that predict the presence or absence of a species are normally judged by the number of prediction errors. These may be of two types: false positives and false negatives. Many of the prediction errors can be traced to ecological processes such as unsaturated habitat and species interactions. Consequently, if prediction errors are not placed in an ecological context the results of the model may be misleading. The simplest, and most widely used, measure of prediction accuracy is the number of correctly classified cases. There are other measures of prediction success that may be more appropriate. Strategies for assessing the causes and costs of these errors are discussed. A range of techniques for measuring error in presence/absence models, including some that are seldom used by ecologists (e.g. ROC plots and cost matrices), are described. A new approach to estimating prediction error, which is based on the spatial characteristics of the errors, is proposed. Thirteen recommendations are made to enable the objective selection of an error assessment technique for ecological presence/absence models},
author = {Fielding, Alan H. and Bell, John.},
journal = {Environ. Conserv.},
number = {1},
pages = {38--49},
title = {{A review of methods for the assessment of prediction errors in conservation presence/absence models}},
volume = {24},
year = {1997}
}

@techreport{Hijmans2011,
author = {Hijmans, R J and Phillips, S and Leathwick, J and Elith, J},
title = {{Package dismo: Species distribution modeling}},
url = {http://cran.r-project.org/web/packages/dismo/index.html.},
year = {2011}
}


@article{Jimenez-Valverde2007,
abstract = {For many applications the continuous prediction afforded by species distribution modeling must be converted to a map of presence or absence, so a threshold probability indicative of species presence must be fixed. Because of the bias in probability outputs due to frequency of presences (prevalence), a fixed threshold value, such as 0.5, does not usually correspond to the threshold above which the species is more likely to be present. In this paper four threshold criteria are compared for a wide range of sample sizes and prevalences, modeling a virtual species in order to avoid the omnipresent error sources that the use of real species data implies. In general, sensitivity-specificity difference minimizer and sensitivity-specificity sum maximizer criteria produced the most accurate predictions. The widely-used 0.5 fixed threshold and Kappa-maximizer criteria are the worst ones in almost all situations. Nevertheless, whatever the criteria used, the threshold value chosen and the research goals that determined its choice must be stated. {\textcopyright} 2007 Elsevier Masson SAS. All rights reserved.},
author = {Jim{\'{e}}nez-Valverde, Alberto and Lobo, Jorge M.},
doi = {10.1016/j.actao.2007.02.001},
isbn = {1146-609X},
issn = {1146609X},
journal = {Acta Oecologica},
keywords = {Confusion matrix,Habitat-suitability models,Kappa statistic,Logistic regression,Sensitivity-specificity difference minimizer,Sensitivity-specificity sum maximizer,Threshold},
pages = {361--369},
pmid = {12412869},
title = {{Threshold criteria for conversion of probability of species presence to either-or presence-absence}},
volume = {31},
year = {2007}
}

@article{Mesgaran2014,
author = {Mesgaran, Mohsen B. and Cousens, Roger D. and Webber, Bruce L.},
doi = {10.1111/ddi.12209},
editor = {Franklin, Janet},
issn = {13669516},
journal = {Divers. Distrib.},
keywords = {Correlation,ExDet,M
ax
E
nt,MESS map,Mahalanobis distance,SDM,model extrapolation,model interrogation,niche modelling,non‐equilibrium settings,novel environment},
month = {oct},
number = {10},
pages = {1147--1159},
publisher = {Wiley/Blackwell (10.1111)},
title = {{Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models}},
url = {http://doi.wiley.com/10.1111/ddi.12209},
volume = {20},
year = {2014}
}


@article{Norris2014,
abstract = {Modeling the distribution of rare and endangered species is challenging, and there is substantial debate regarding what species distribution models (SDMs) actually represent. Here I investigated whether locations of different lowland tapir signs (feces, trails and tracks) generated different distributions of suitable habitat using a presence-only species distribution modeling technique. Comparison of the equivalence and overlap of the predicted distributions showed no significant differences between the different signs. The contribution of the 11 variables used to build the distribution models was also similar between signs. Although predictions from different signs were similar, the use of different threshold selection methods generated substantially different suitable areas and omission errors. These results highlight the importance of a fundamental understanding of species natural history to determine not only appropriate model parameters, but also the biological relevance of SDMs. My findings also support the need for healthy skepticism regarding what is represented by presence-only species distributions. To help address this skepticism I conclude by providing guidelines for generating reliable local-scale distribution models. {\textcopyright} Darren Norris.},
author = {Norris, Darren},
doi = {10.1177/194008291400700311},
issn = {19400829},
journal = {Trop. Conserv. Sci.},
keywords = {Atlantic Forest,Habitat suitability,MaxEnt,Species distribution modeling,Tapirus terrestris},
number = {3},
pages = {529--547},
title = {{Model thresholds are more important than presence location type: Understanding the distribution of lowland tapir (Tapirus terrestris) in a continuous Atlantic forest of southeast Brazil}},
volume = {7},
year = {2014}
}

@article{Owens2013,
author = {Owens, Hannah L and Campbell, Lindsay P and Dornak, L Lynnette and Saupe, Erin E and Barve, Narayani and Sober{\'{o}}n, Jorge and Ingenloff, Kate and Lira-Noriega, Andr{\'{e}}s and Hensz, Christopher M and Myers, Corinne E and Peterson, A Townsend},
doi = {http://dx.doi.org/10.1016/j.ecolmodel.2013.04.011},
isbn = {0304-3800},
journal = {Ecol. Modell.},
keywords = {Ecological niche model,Extrapolation,Mobility-Oriented Parity,Model transfer,Multivariate Environmental Similarity Surface,Species distribution model},
number = {0},
pages = {10--18},
title = {{Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas}},
url = {http://www.sciencedirect.com/science/article/pii/S0304380013002159},
volume = {263},
year = {2013}
}


@article{Peterson2008,
abstract = {The area under the curve (AUC) of the receiver operating characteristic (ROC) has become a dominant tool in evaluating the accuracy of models predicting distributions of species. ROC has the advantage of being threshold-independent, and as such does not require decisions regarding thresholds of what constitutes a prediction of presence versus a prediction of absence. However, we show that, comparing two ROCs, using the AUC systematically undervalues models that do not provide predictions across the entire spectrum of proportional areas in the study area. Current ROC approaches in ecological niche modeling applications are also inappropriate because the two error components are weighted equally. We recommend a modification of ROC that remedies These problems, using partial-area ROC approaches to provide a firmer foundation for evaluation of predictions from ecological niche models. A worked example demonstrates that models that are evaluated favorably by traditional ROC AUCs are not necessarily the best when niche modeling considerations are incorporated into the design of the test. {\textcopyright} 2007 Elsevier B.V. All rights reserved.},
author = {Peterson, A. Townsend and Papes, Monica and Soberon, Jorge},
journal = {Ecol. Modell.},
keywords = {Area under curve,Ecological niche model,Model evaluation,Omission error,Receiver operating characteristic},
number = {1},
pages = {63--72},
title = {{Rethinking receiver operating characteristic analysis applications in ecological niche modeling}},
volume = {213},
year = {2008}
}

@book{Peterson2011b,
abstract = {This book provides a first synthetic view of an emerging area of ecology and biogeography, linking individual- and population-level processes to geographic distributions and biodiversity patterns. Problems in evolutionary ecology, macroecology, and biogeography are illuminated by this integrative view. The book focuses on correlative approaches known as ecological niche modeling, species distribution modeling, or habitat suitability modeling, which use associations between known occurrences of species and environmental variables to identify environmental conditions under which populations can be maintained. The spatial distribution of environments suitable for the species can then be estimated: a potential distribution for the species. This approach has broad applicability to ecology, evolution, biogeography, and conservation biology, as well as to understanding the geographic potential of invasive species and infectious diseases, and the biological implications of climate change. The authors lay out conceptual foundations and general principles for understanding and interpreting species distributions with respect to geography and environment. Focus is on development of niche models. While serving as a guide for students and researchers, the book also provides a theoretical framework to support future progress in the field.},
author = {Peterson, A. Townsend and Sober{\'{o}}n, Jorge and Pearson, Richard G and Anderson, Robert P and Mart{\'{i}}nez-Meyer, E and Nakamura, Miguel and {Bastos Araujo}, Miguel},
booktitle = {Princet. Univ. Press},
doi = {10.5860/CHOICE.49-6266},
isbn = {9780691136882},
issn = {0009-4978},
pages = {328},
pmid = {23580187},
publisher = {Princeton University Press},
title = {{Ecological niches and geographic distributions.}},
url = {http://www.cro3.org/cgi/doi/10.5860/CHOICE.49-6266},
year = {2011}
}

@article{Peterson2001b,
author = {Peterson, A. Townsend and Vieglais, David A.},
doi = {10.1641/0006-3568(2001)051[0363:psiuen]2.0.co;2},
file = {::},
issn = {0006-3568},
journal = {Bioscience},
month = {may},
number = {5},
pages = {363--371},
publisher = {Oxford University Press},
title = {{Predicting Species Invasions Using Ecological Niche Modeling}},
url = {https://academic.oup.com/bioscience/article/51/5/363/243986},
volume = {51},
year = {2001}
}

@inproceedings{Phillips2004,
address = {Banff, Canada},
author = {Phillips, S and Dudik, M and Schapire, R},
booktitle = {21st Int. Conf. Mach. Learn.},
title = {{A maximum entropy approach to species distribution modeling}},
year = {2004}
}

@article{Phillips2008,
abstract = {Accurate modeling of geographic distributions of species is crucial$\backslash$nto various applications in ecology and conservation. The best performing$\backslash$ntechniques often require some parameter tuning, which may be prohibitively$\backslash$ntime-consuming to do separately for each species, or unreliable for$\backslash$nsmall or biased datasets. Additionally, even with the abundance of$\backslash$ngood quality data, users interested in the application of species$\backslash$nmodels need not have the statistical knowledge required for detailed$\backslash$ntuning. In such cases, it is desirable to use “default settings�?,$\backslash$ntuned and validated on diverse datasets. Maxent is a recently introduced$\backslash$nmodeling technique, achieving high predictive accuracy and enjoying$\backslash$nseveral additional attractive properties. The performance of Maxent$\backslash$nis influenced by a moderate number of parameters. The first contribution$\backslash$nof this paper is the empirical tuning of these parameters. Since$\backslash$nmany datasets lack information about species absence, we present$\backslash$na tuning method that uses presence-only data. We evaluate our method$\backslash$non independently collected high-quality presence-absence data. In$\backslash$naddition to tuning, we introduce several concepts that improve the$\backslash$npredictive accuracy and running time of Maxent. We introduce “hinge$\backslash$nfeatures�? that model more complex relationships in the training$\backslash$ndata; we describe a new logistic output format that gives an estimate$\backslash$nof probability of presence; finally we explore “background sampling�?$\backslash$nstrategies that cope with sample selection bias and decrease model-building$\backslash$ntime. Our evaluation, based on a diverse dataset of 226 species from$\backslash$n6 regions, shows: 1) default settings tuned on presence-only data$\backslash$nachieve performance which is almost as good as if they had been tuned$\backslash$non the evaluation data itself; 2) hinge features substantially improve$\backslash$nmodel performance; 3) logistic output improves model calibration,$\backslash$nso that large differences in output values correspond better to large$\backslash$ndifferences in suitability; 4) “target-group�? background sampling$\backslash$ncan give much better predictive performance than random background$\backslash$nsampling; 5) random background sampling results in a dramatic decrease$\backslash$nin running time, with no decrease in model performance.},
author = {Phillips, Steven J. and Dud{\'{i}}k, Miroslav},
journal = {Ecography (Cop.).},
number = {2},
pages = {161--175},
title = {{Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation}},
volume = {31},
year = {2008}
}


@article{VanAelst2009,
author = {{Van Aelst}, Stefan and Rousseeuw, Peter},
doi = {10.1002/wics.19},
issn = {1939-0068},
journal = {Wiley Interdiscip. Rev. Comput. Stat.},
keywords = {affine equivariance,high breakdown,multivariate location and scatter,outlier detection,robustness},
month = {jul},
number = {1},
pages = {71--82},
publisher = {John Wiley {\&} Sons, Inc.},
title = {{Minimum volume ellipsoid}},
url = {http://dx.doi.org/10.1002/wics.19},
volume = {1},
year = {2009}
}

@article{PetersonT.2001,
author = {{Peterson  T.}, A and Vieglais, David},
journal = {Bioscience},
pages = {363--371},
title = {{Predicting species invasions using ecological niche modeling}},
volume = {51},
year = {2001}
}
